hal9001: Highly adaptive lasso regression in R


The hal9001 R package provides a computationally efficient implementation of the highly adaptive lasso, a flexible nonparametric regression and machine learning algorithm endowed with several theoretically convenient properties. hal9001 pairs an implementation of this estimator with an array of practical variable selection tools and sensible defaults in order to improve the scalability of the algorithm. By building on existing R packages for lasso regression and leveraging compiled code in key internal functions, the hal9001 R package provides a family of highly adaptive lasso estimators suitable for use in both modern large-scale data analysis and cutting-edge research efforts at the intersection of statistics and machine learning, including the emerging subfield of computational causal inference.

In Journal of Open Source Software
Nima Hejazi
Nima Hejazi
Assistant Professor of Biostatistics

My research lies at the intersection of causal inference and machine learning, developing flexible methodology for statistical inference tailored to modern experiments and observational studies in the biomedical and public health sciences.